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Retrofit Recommender at a glance
The Retrofit Recommender is a machine-learning driven tool designed to assist property and asset managers in promptly identifying potential areas for capital improvements (retrofits) in buildings. With minimal input, the tool generates needed capex investments and potential energy savings, enabling users to evaluate options for increasing the property's overall value, extending its useful life, and improving energy and cost efficiency.
the Retrofit Recommender
This easy-to-use recommendation engine takes into account a variety of building-specific variables including:
Asset type (as per GRESB definitions)
Current energy consumption data (not available in our free online version, only via full online platform)
Our scenario modeling algorithm trained on hundreds of real retrofit datapoints and outcomes, generates top-line retrofit scenarios to help stakeholders make critical decisions to protect their properties' value. The recommended measures align retrofit scenarios with users objectives, whether those are related to a CapEx ceiling or a CO2 reduction floor.
Our tool's recommendations emphasize energy savings and heating source optimization (including heating oil, heat pumps, natural gas, and district heating) while suggesting potential enhancements to heating, cooling, ventilation, and lighting systems. It also examines sub-categories within the building's thermal envelope, such as the roof, façade, and windows.
Calculate your retrofitLight versionLight
Try out a light version of the tool below and understand the value BuildingMinds’ machine learning can bring to your portfolio through risk avoidance and better investment strategies for existing holdings.
Make a choice
Select your building type
How it works
Based on your building information, the Retrofit Recommender calculates output estimations and our machine learning tool helps to test different scenarios, so that you can plan your next retrofit.
Enter your own parameters
such as Construction Year and Building Type
Get output estimations
or fix factors related to:
• Retrofit areas with best return for carbon impact
• CO2 Reduction measures
Specify one or more output to generate estimates of the others.
Test the ML-driven scenarios with different variables.
Plan your next retrofit
The model predicts retrofit actions, estimates energy savings and costs of the retrofit measures based on the building’s attributes (standard input):
building type (office, residential, etc.)
building area (in m2)
location (country, according to ISO-3166 ALPHA-2 code, e.g. DE)
primary heating source (optional)
heating and electricity consumption before retrofit (kWh/m2 per year).